How can I adapt it for a continuous action space problem?

How can I adapt it for a continuous action space problem?

How can I adapt it for a continuous action space problem such as Pendulum v0. That seems strange since I imagined the curve of actions to be skewed one way or another depending on whether a particular state would get better results with certain action tendencies. Update: I found this on the Stable baselines site for PPO:

When do you need to use normalization and standardization?

When Should You Use Normalization And Standardization: Normalizationis a good technique to use when you do not know the distribution of your data or when you know the distribution is not Gaussian (a bell curve).

What does it mean to normalize a vector?

“Normalizing”a vector most often means dividing by a norm of the vector. It also often refers to rescaling by the minimum and range of the vector, to make all the elements lie between 0 and 1 thus bringing all the values of numeric columns in the dataset to a common scale.

When do you standardize the features around the center?

Standardizing the features around the center and 0 with a standard deviation of 1 is important when we compare measurements that have different units. Variables that are measured at different scales do not contribute equally to the analysis and might end up creating a bais.

How does ordinal parameterization improve PPO / Trpo performance?

Additionally, we show that an or- dinal parameterization of the discrete distribution can introduce the inductive bias that encodes the natural ordering between discrete actions. This ordinal architecture further significantly improves the performance of PPO/TRPO. 1. Background

How is action space dichotomized in reinforcement learning?

In reinforcement learning (RL), the action space of con- ventional control tasks are usually dichotomized into either discrete or continuous (Brockman et al.,2016).

How to implement the PPO clipping part of the algorithm?

Then I worked out the ratio of action probabilities and implemented the PPO clipping parts of the algorithm: The full code is here (please excuse some coarse language in the comments): https://github.com/nyck33/openai_my_implements/blob/master/cartpole/my_ppo_cartpole.py